Learning desk

MultiAgent EDU StackGather good sources. Teach what matters.
T5TauricResearch/TradingAgentsT5A Man Who Invented Modern AI (Before Everyone Else) – Jürgen Schmidhuber [video]T5GPT-4 finished training four years ago todayT5AI Settles a 25 Year-Old Problem We Left BehindT5What it was like working on LLMs and security at Meta (2022-2026)T5Ask HN: How do you go from writing code to deploying with agents?T5What Happened: OpenAI and HuggingFaceT5Apple says Mac users in China can connect to Alibaba's Qwen AI serviceT5Show HN: Try Benzi – A coding harness/agent beating Claude Code itself on SonnetT5The AI Apocalypse Is HereT3Auto mode is now the default in Claude Code for Pro, Max, and Team plansT5Show HN: Tura – Build agent that uses 80% less token and delivers better resultsT5TauricResearch/TradingAgentsT5A Man Who Invented Modern AI (Before Everyone Else) – Jürgen Schmidhuber [video]T5GPT-4 finished training four years ago todayT5AI Settles a 25 Year-Old Problem We Left BehindT5What it was like working on LLMs and security at Meta (2022-2026)T5Ask HN: How do you go from writing code to deploying with agents?T5What Happened: OpenAI and HuggingFaceT5Apple says Mac users in China can connect to Alibaba's Qwen AI serviceT5Show HN: Try Benzi – A coding harness/agent beating Claude Code itself on SonnetT5The AI Apocalypse Is HereT3Auto mode is now the default in Claude Code for Pro, Max, and Team plansT5Show HN: Tura – Build agent that uses 80% less token and delivers better results
← Dispatches

Extending the UTAUT model to explore the acceptance and use of generative AI: the roles of generative AI identity and trust

Primary research

#1318

T1new
Topic
unassigned (set during synthesis)
First seen
2026-08-05 07:16:31
Last seen
2026-08-05 07:16:31

Source raw items (1)

  • Semantic Scholar2026-08-05 07:15:58
    Extending the UTAUT model to explore the acceptance and use of generative AI: the roles of generative AI identity and trust

    Generative AI (GenAI) technology has been rapidly integrated into diverse domains, including education. It has fundamentally reshaped the global learning landscape, exerting a significantly positive influence on students’ learning experience. However, the factors influencing adult learners’ behavioral intention to use GenAI have not yet been fully understood. Therefore, this study aims to explore key factors influencing the behavioral intention to use GenAI among adult learners from central Taiwan. Based on the unified theory of acceptance and use of technology (UTAUT) model, this study integrated two constructs—GenAI identity and trust—to extend the model. A structured questionnaire survey was performed to collect data from 718 adult learners who were aged 50 or above and from central Taiwan. Partial least squares (PLS) and Partial least squares-Multi-group analyses (PLS-MGA) were employed for data analysis. The following results were obtained: (1) social influence and facilitating conditions are significant antecedent factors influencing adult learners’ behavioral intention to use GenAI—conversely, performance expectancy has a negative influence on such intention; (2) GenAI identity and trust are key predictive variables of performance expectancy and effort expectancy; (3) different gender groups exhibit significant differences in most structural paths of the research model. These findings provide theoretical support for context-sensitive strategies in adult learning and the educational application of GenAI.